# Comparison of definitions for Functions of Bounded Variation

I have been trying to understand the functions of bounded variation and I came across the following definitions

Defintion 1: A function $$f:\mathbb{R^d} \rightarrow \mathbb{R}$$ is of bounded variation iff $$\begin{split} \operatorname{TV}(f)&:=\int\limits_{\mathbb{R}^{d-1}}\mathcal{TV}(f(\cdot,x_2,\cdots,x_d))dx_2 \cdots dx_m +\cdots +\\ & \quad+\cdots+\int\limits_{\mathbb{R}^{d-1}}\mathcal{TV}(f(x_1, \cdots, x_{d-1},\cdot)) dx_1\cdots dx_{d-1} < \infty. \end{split}$$

where, for $$g:\mathbb{R} \rightarrow \mathbb{R}$$ $$\mathcal{TV}(g):=\sup \left\{\sum\limits_{k=1}^N{\left|g(\xi_k)-g(\xi_{k-1})\right|}\right\}$$ and supremum is taken over all $$M \geq 1$$ and all partitions $$\{\xi_1,\xi_2,....,\xi_N\}$$ of $$\mathbb{R}.$$

Defintion 2: A function $$f:\mathbb{R^d} \rightarrow \mathbb{R}$$ is of bounded variation iff

$$\operatorname{TV}(f)= \sup \left\{\,\int\limits_{\mathbb{R}^m}f \operatorname{div}(\phi): \phi \in C_c^1(\mathbb{R^d})^d, \|\phi\|_{L^{\infty}} \leq 1\, \right\} < \infty.$$

Clearly if $$f$$ is of bounded variation in the sense of definition 2, it may not be of bounded variation in the sense of definition 1.

In this regard, I have the following doubts.

1. If $$f$$ satisfies definition 1, then do we have $$f$$ satisfies definition 2? (I felt so but could not prove it rigorously).
2. If [1] is true then $$\operatorname{TV}(f)$$ calculated by definition 1 and definition 2 are they equal?
3. If $$f$$ satisfies definition 2, does there exist a function $$g:\mathbb{R}^d \rightarrow \mathbb{R}$$ a.e equal to $$f$$ such that $$g$$ satisfies definition 1? If so how to prove it?

P.S. : I have read somewhere that 3 is true in one dimension and in-fact we can find $$g$$ which is right continuous. But I could not find the rigorous proof and also I could not find any such result in multi-d.

• Are both definitions equivalent for $d = 1$? In higher dimensions, I'd try using $\Phi = \phi_1e_1+\cdots\phi_ne_n$, with $\phi_i$ a suitable choice. Can you prove something under the assumption that $f$ is smooth? – user90189 Apr 2 at 13:01

The questions, despite looking as a representation problem in functional analysis, are much deeper as they bring out the history of the topic involved, notably $$BV$$-functions and the reasons why the customary definition adopted for the variation of a multivariate function is definition 2 above. And as thus the answers below needs to indulge a bit on this history: said that, let's start.

1. If $$f$$ satisfies definition 1, then do we have $$f$$ satisfies definition 2? (I felt so but could not prove it rigorously).

No: the two definitions are in general not equivalent. The main problem is that definition 1 is not invariant respect to coordinate changes for all $$L^1$$ functions: in particular, there exists functions for which the value of the variation $$\mathrm{TV}(f)$$ depend on the choice of coordinate axes, as shown by by Adams and Clarkson ([1], pp. 726-727) with their counterexample. Precisely, by using the ternary set, they construct a function of two variables such that the total variation according to definition 1 passes from a finite value to an infinite one simply by a rotation of angle $${\pi}/{4}$$ of the coordinate axes.
However, for particular classes of functions, the answer is yes: this happens for example for continuous functions, as Leonida Tonelli was well aware of when he introduced definition 1. We'll see something more in the joint answer to the second and third questions.

1. If [1] is true then $$\operatorname{TV}(f)$$ calculated by definition 1 and definition 2 are they equal?
2. If $$f$$ satisfies definition 2, does there exist a function $$g:\mathbb{R}^d \rightarrow \mathbb{R}$$ a.e equal to $$f$$ such that $$g$$ satisfies definition 1? If so how to prove it?

Since definition 1 is not coordinate invariant in $$L^1$$ while definition 2 is, for questions 2 and 3 the answer is no. However, things change if, instead of the total (pointwise) variation $$\mathcal{TV}$$, one considers the essential variation defined as $$\newcommand{\eV}{\mathrm{essV}} \eV(g):=\inf \left\{\mathcal{TV}(v) : g=v\;\; L^1\text{-almost everywhere (a.e.) in }\Bbb R\right\}$$ (see [2], §3.2, p. 135 or [4], §5.3, p. 227 for an alternative definition involving approximate continuity, closer to the original Lamberto Cesari's approach). Then you have the following theorem

Theorem 5.3.5 ([4], pp. 227-228) Let $$f\in L^1_\text{loc}(\mathbb{R}^n)$$. Then $$f\in BV_\text{loc}(\Bbb R^n)$$ if and only if $$\int\limits_{R^{n-1}}\eV_i\big(f(x)\big)\,{\mathrm{d}} x_1\cdots{\mathrm{d}}x_{i-1}\cdot {\mathrm{d}}x_{i+1}\cdots {\mathrm{d}}x_n< \infty\quad \forall i=1,\ldots,n$$ where

• $$\eV_i\big(f(x)\big)$$ is the essential variation of the one dimensional sections of $$f$$ along the $$i$$-axis and
• $$R^{n-1}\subset \Bbb R^{n-1}$$ is any $$(n-1)$$-dimensional hypercube.

This result, apart from its intrinsic interest, is valuable since it allows two prove a variation of the sought for result: namely $$\sum_{i=1}^n \int\limits_{R^{n-1}}\eV_i\big(f(x)\big)\,{\mathrm{d}} x_1\cdots{\mathrm{d}}x_{i-1}\cdot {\mathrm{d}}x_{i+1}\cdots {\mathrm{d}}x_n =\sup \left\{\,\int\limits_{\mathbb{R}^m}f \operatorname{div}(\phi): \phi \in C_c^1(\mathbb{R^d})^d, \|\phi\|_{L^{\infty}} \leq 1\, \right\}\label{1}\tag{V}$$ The proof of \eqref{1} follows from the proof of theorem 5.3.5 above in that the method is the same but, instead of the single $$i$$-th axis ($$i=1,\ldots,n$$) essential variation, the sum of the $$n$$ essential variations is considered. Also, both sides of equation \eqref{1} are lower semicontinuous thus, given any sequence of $$BV$$ functions $$\{f_j\}_{j\in\Bbb N}$$ for which they converge to a common (finite) value, it is possible to find a subsequence converging to a $$BV$$ function $$f$$: simply stated, the supremum is attained for the limit function of the subsequence and thus it is a maximum. Thus question 2 and 3 have an affirmative answer if the essential variation is considered instead of the (pointwise) total variation.

Notes

• Definition 1 defines the so called "total variation in the sense of Tonelli", and was introduced by Leonida Tonelli only for continuous functions, since the problem of non invariance of the value of variation respect to a change in coordinate axes, pointed out by Adams and Clarkson ([1], pp 726-727), does not exists in this class. The multidimensional total variation defined by using the essential variation, i.e. $$\mathrm{TV}(f)=\sum_{i=1}^n \int\limits_{R^{n-1}}\eV_i\big(f(x)\big)\,{\mathrm{d}} x_1\cdots{\mathrm{d}}x_{i-1}\cdot {\mathrm{d}}x_{i+1}\cdots {\mathrm{d}}x_n$$ is called the "total variation in the sense of Tonelli and Cesari" and was introduced by Lamberto Cesari in [3], pp. 299-300 to overcome the known limitation of definition 1.

• I predated reference [1] from the answer by @Piotr Hajlasz to this Q&A: as I pointed out there, definition 1 is the original definition of bounded variation for functions of several variables given by Lamberto Cesari in 1936. Definition 2 was introduced later by Mario Miranda in the early sixties of the 20th century.

References

[1] C. Raymond Adams, James A. Clarkson, "Properties of functions $$f(x,y)$$ of bounded variation" (English), Transactions of the American Mathematical Society 36, 711-730 (1934), MR1501762, Zbl 0010.19902.

[2] Luigi Ambrosio, Nicola Fusco, Diego Pallara, Functions of bounded variation and free discontinuity problems, Oxford Mathematical Monographs, New York and Oxford: The Clarendon Press/Oxford University Press, New York, pp. xviii+434 (2000), ISBN 0-19-850245-1, MR1857292, Zbl 0957.49001.

[3] Lamberto Cesari, "Sulle funzioni a variazione limitata" (Italian), Annali della Scuola Normale Superiore, Serie II, 5 (3–4), 299–313 (1936), JFM , MR1556778, Zbl 0014.29605

[4] William P. Ziemer, Weakly differentiable functions. Sobolev spaces and functions of bounded variation. Graduate Texts in Mathematics, 120. New York: Springer-Verlag, pp. xvi+308, 1989, ISBN: 0-387-97017-7, MR1014685, Zbl 0692.46022

The answer to all three questions is yes, but there are some subtleties. In Definition 2 if you modify the function on a set of measure zero, $$TV$$ does not change. So you need to keep into account sets of measure zero in Definition 1 when $$d>1$$. The idea is to change $$\mathcal{TV}$$ and instead of using arbitrary partitions you only use partitions made of points which are Lebesgue points of your function. This is is called the essential pointwise variation of the function. You can find these results in Leoni The case $$d=1$$ is Theorem 7.3 and is exactly what you wrote. For $$d>1$$ the result you want is due to Serrin and is given in Theorem 14.20 using the essential pointwise variation instead of the pointwise variation. For $$d=1$$ you can also look at Evans and Gariepy Theorem 5.21. Unfortunately none of the proofs are easy.